Lab 5
Andrea Dame
9/21/2020
library(tidyverse)## ── Attaching packages ───────────────────────────────── tidyverse 1.3.0 ──## ✔ ggplot2 3.2.1 ✔ purrr 0.3.4
## ✔ tibble 3.0.3 ✔ dplyr 1.0.2
## ✔ tidyr 1.1.2 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.5.0## ── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()Confirmed_State_3_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-13-2020.csv")) %>%
rename(Country_Region = "Country/Region", Province_State = "Province/State") %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) str(Confirmed_State_3_13)## tibble [53 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:53] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:53] "US" "US" "US" "US" ...
## $ Confirmed : num [1:53] 5 1 9 6 282 49 11 4 46 10 ...
## - attr(*, "groups")= tibble [53 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:53] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:53]
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## ..- attr(*, ".drop")= logi TRUEConfirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) str(Confirmed_State_9_13)## tibble [58 × 3] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Country_Region: chr [1:58] "US" "US" "US" "US" ...
## $ Confirmed : num [1:58] 138755 6268 208512 70219 761728 ...
## - attr(*, "groups")= tibble [58 × 2] (S3: tbl_df/tbl/data.frame)
## ..$ Province_State: chr [1:58] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## ..$ .rows : list<int> [1:58]
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## ..- attr(*, ".drop")= logi TRUEsetdiff(Confirmed_State_9_13$Province_State, Confirmed_State_3_13$Province_State)## [1] "Guam" "Northern Mariana Islands"
## [3] "Puerto Rico" "Recovered"
## [5] "Virgin Islands"Confirmed_State_9_13 <- Confirmed_State_9_13 %>%
filter(Province_State != "Recovered") Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State"))
head(Confirmed_State_3_13_9_13_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 5 US 138755
## 2 Alaska US 1 US 6268
## 3 Arizona US 9 US 208512
## 4 Arkansas US 6 US 70219
## 5 California US 282 US 761728
## 6 Colorado US 49 US 61293tail(Confirmed_State_3_13_9_13_joined, 5)## # A tibble: 5 x 5
## # Groups: Province_State [5]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Wyoming US 1 US 4346
## 2 Guam <NA> NA US 1863
## 3 Northern Maria… <NA> NA US 60
## 4 Puerto Rico <NA> NA US 37380
## 5 Virgin Islands <NA> NA US 1220which(is.na(Confirmed_State_3_13_9_13_joined))## [1] 111 112 113 114 168 169 170 171Confirmed_State_3_13_9_13_joined <- full_join(Confirmed_State_3_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_3_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_3_13_2020 = 0))
head(Confirmed_State_3_13_9_13_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_3_13_2020 Confirmed_9_13_2020
## <chr> <dbl> <dbl>
## 1 Alabama 5 138755
## 2 Alaska 1 6268
## 3 Arizona 9 208512
## 4 Arkansas 6 70219
## 5 California 282 761728
## 6 Colorado 49 61293which(is.na(Confirmed_State_3_13_9_13_joined))## integer(0)Switching between wide and long table formats.
.
Confirmed_State_3_13_9_13_joined_long <- Confirmed_State_3_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")# to adjust figure size {r, fig.width = 5, fig.height = 10}
ggplot(Confirmed_State_3_13_9_13_joined_long, aes(x = Confirmed, y = Province_State)) +
geom_point(aes(color = Date)) 
Working with the time series data
Data Wrangling
time_series_confirmed <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region") download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
destfile = "data/time_series_covid19_confirmed_global.csv")time_series_confirmed <- read_csv("data/time_series_covid19_confirmed_global.csv")%>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.head(time_series_confirmed)## # A tibble: 6 x 249
## Province_State Country_Region Lat Long `1/22/20` `1/23/20` `1/24/20`
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 0 0 0
## 2 <NA> Albania 41.2 20.2 0 0 0
## 3 <NA> Algeria 28.0 1.66 0 0 0
## 4 <NA> Andorra 42.5 1.52 0 0 0
## 5 <NA> Angola -11.2 17.9 0 0 0
## 6 <NA> Antigua and B… 17.1 -61.8 0 0 0
## # … with 242 more variables: `1/25/20` <dbl>, `1/26/20` <dbl>,
## # `1/27/20` <dbl>, `1/28/20` <dbl>, `1/29/20` <dbl>, `1/30/20` <dbl>,
## # `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>, `2/3/20` <dbl>,
## # `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>, `2/7/20` <dbl>,
## # `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>, `2/11/20` <dbl>,
## # `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>, `2/15/20` <dbl>,
## # `2/16/20` <dbl>, `2/17/20` <dbl>, `2/18/20` <dbl>, `2/19/20` <dbl>,
## # `2/20/20` <dbl>, `2/21/20` <dbl>, `2/22/20` <dbl>, `2/23/20` <dbl>,
## # `2/24/20` <dbl>, `2/25/20` <dbl>, `2/26/20` <dbl>, `2/27/20` <dbl>,
## # `2/28/20` <dbl>, `2/29/20` <dbl>, `3/1/20` <dbl>, `3/2/20` <dbl>,
## # `3/3/20` <dbl>, `3/4/20` <dbl>, `3/5/20` <dbl>, `3/6/20` <dbl>,
## # `3/7/20` <dbl>, `3/8/20` <dbl>, `3/9/20` <dbl>, `3/10/20` <dbl>,
## # `3/11/20` <dbl>, `3/12/20` <dbl>, `3/13/20` <dbl>, `3/14/20` <dbl>,
## # `3/15/20` <dbl>, `3/16/20` <dbl>, `3/17/20` <dbl>, `3/18/20` <dbl>,
## # `3/19/20` <dbl>, `3/20/20` <dbl>, `3/21/20` <dbl>, `3/22/20` <dbl>,
## # `3/23/20` <dbl>, `3/24/20` <dbl>, `3/25/20` <dbl>, `3/26/20` <dbl>,
## # `3/27/20` <dbl>, `3/28/20` <dbl>, `3/29/20` <dbl>, `3/30/20` <dbl>,
## # `3/31/20` <dbl>, `4/1/20` <dbl>, `4/2/20` <dbl>, `4/3/20` <dbl>,
## # `4/4/20` <dbl>, `4/5/20` <dbl>, `4/6/20` <dbl>, `4/7/20` <dbl>,
## # `4/8/20` <dbl>, `4/9/20` <dbl>, `4/10/20` <dbl>, `4/11/20` <dbl>,
## # `4/12/20` <dbl>, `4/13/20` <dbl>, `4/14/20` <dbl>, `4/15/20` <dbl>,
## # `4/16/20` <dbl>, `4/17/20` <dbl>, `4/18/20` <dbl>, `4/19/20` <dbl>,
## # `4/20/20` <dbl>, `4/21/20` <dbl>, `4/22/20` <dbl>, `4/23/20` <dbl>,
## # `4/24/20` <dbl>, `4/25/20` <dbl>, `4/26/20` <dbl>, `4/27/20` <dbl>,
## # `4/28/20` <dbl>, `4/29/20` <dbl>, `4/30/20` <dbl>, `5/1/20` <dbl>,
## # `5/2/20` <dbl>, `5/3/20` <dbl>, …time_series_confirmed_long <- time_series_confirmed %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Confirmed") head(time_series_confirmed_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0 download.file(url="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
destfile = "data/time_series_covid19_deaths_global.csv")time_series_deaths <- read_csv("data/time_series_covid19_deaths_global.csv") %>%
rename(Province_State = "Province/State", Country_Region = "Country/Region")## Parsed with column specification:
## cols(
## .default = col_double(),
## `Province/State` = col_character(),
## `Country/Region` = col_character()
## )## See spec(...) for full column specifications.time_series_deaths_long <- time_series_deaths %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long),
names_to = "Date", values_to = "Deaths")
head(time_series_deaths_long)## # A tibble: 6 x 6
## Province_State Country_Region Lat Long Date Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0Joining the time series tables
time_series_confirmed_long <- time_series_confirmed_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
head(time_series_confirmed_long)## # A tibble: 6 x 7
## Key Province_State Country_Region Lat Long Date Confirmed
## <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 NA.Afghanista… <NA> Afghanistan 33.9 67.7 1/22/… 0
## 2 NA.Afghanista… <NA> Afghanistan 33.9 67.7 1/23/… 0
## 3 NA.Afghanista… <NA> Afghanistan 33.9 67.7 1/24/… 0
## 4 NA.Afghanista… <NA> Afghanistan 33.9 67.7 1/25/… 0
## 5 NA.Afghanista… <NA> Afghanistan 33.9 67.7 1/26/… 0
## 6 NA.Afghanista… <NA> Afghanistan 33.9 67.7 1/27/… 0time_series_deaths_long <- time_series_deaths_long %>%
unite(Key, Province_State, Country_Region, Date, sep = ".") %>%
select(Key, Deaths)time_series_long_joined <- full_join(time_series_confirmed_long,
time_series_deaths_long, by = c("Key")) %>%
select(-Key)
head(time_series_long_joined)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 1/22/20 0 0
## 2 <NA> Afghanistan 33.9 67.7 1/23/20 0 0
## 3 <NA> Afghanistan 33.9 67.7 1/24/20 0 0
## 4 <NA> Afghanistan 33.9 67.7 1/25/20 0 0
## 5 <NA> Afghanistan 33.9 67.7 1/26/20 0 0
## 6 <NA> Afghanistan 33.9 67.7 1/27/20 0 0which(is.na(time_series_long_joined$Confirmed))## [1] 65171 65172 65173 65174 65175 65176 65177 65178 65179 65180 65181
## [12] 65182 65183 65184 65185 65186 65187 65188 65189 65190 65191 65192
## [23] 65193 65194 65195 65196 65197 65198 65199 65200 65201 65202 65203
## [34] 65204 65205 65206 65207 65208 65209 65210 65211 65212 65213 65214
## [45] 65215 65216 65217 65218 65219 65220 65221 65222 65223 65224 65225
## [56] 65226 65227 65228 65229 65230 65231 65232 65233 65234 65235 65236
## [67] 65237 65238 65239 65240 65241 65242 65243 65244 65245 65246 65247
## [78] 65248 65249 65250 65251 65252 65253 65254 65255 65256 65257 65258
## [89] 65259 65260 65261 65262 65263 65264 65265 65266 65267 65268 65269
## [100] 65270 65271 65272 65273 65274 65275 65276 65277 65278 65279 65280
## [111] 65281 65282 65283 65284 65285 65286 65287 65288 65289 65290 65291
## [122] 65292 65293 65294 65295 65296 65297 65298 65299 65300 65301 65302
## [133] 65303 65304 65305 65306 65307 65308 65309 65310 65311 65312 65313
## [144] 65314 65315 65316 65317 65318 65319 65320 65321 65322 65323 65324
## [155] 65325 65326 65327 65328 65329 65330 65331 65332 65333 65334 65335
## [166] 65336 65337 65338 65339 65340 65341 65342 65343 65344 65345 65346
## [177] 65347 65348 65349 65350 65351 65352 65353 65354 65355 65356 65357
## [188] 65358 65359 65360 65361 65362 65363 65364 65365 65366 65367 65368
## [199] 65369 65370 65371 65372 65373 65374 65375 65376 65377 65378 65379
## [210] 65380 65381 65382 65383 65384 65385 65386 65387 65388 65389 65390
## [221] 65391 65392 65393 65394 65395 65396 65397 65398 65399 65400 65401
## [232] 65402 65403 65404 65405 65406 65407 65408 65409 65410 65411 65412
## [243] 65413 65414 65415 65416 65417 65418 65419 65420 65421 65422 65423
## [254] 65424 65425 65426 65427 65428 65429 65430 65431 65432 65433 65434
## [265] 65435 65436 65437 65438 65439 65440 65441 65442 65443 65444 65445
## [276] 65446 65447 65448 65449 65450 65451 65452 65453 65454 65455 65456
## [287] 65457 65458 65459 65460 65461 65462 65463 65464 65465 65466 65467
## [298] 65468 65469 65470 65471 65472 65473 65474 65475 65476 65477 65478
## [309] 65479 65480 65481 65482 65483 65484 65485 65486 65487 65488 65489
## [320] 65490 65491 65492 65493 65494 65495 65496 65497 65498 65499 65500
## [331] 65501 65502 65503 65504 65505 65506 65507 65508 65509 65510 65511
## [342] 65512 65513 65514 65515 65516 65517 65518 65519 65520 65521 65522
## [353] 65523 65524 65525 65526 65527 65528 65529 65530 65531 65532 65533
## [364] 65534 65535 65536 65537 65538 65539 65540 65541 65542 65543 65544
## [375] 65545 65546 65547 65548 65549 65550 65551 65552 65553 65554 65555
## [386] 65556 65557 65558 65559 65560 65561 65562 65563 65564 65565 65566
## [397] 65567 65568 65569 65570 65571 65572 65573 65574 65575 65576 65577
## [408] 65578 65579 65580 65581 65582 65583 65584 65585 65586 65587 65588
## [419] 65589 65590 65591 65592 65593 65594 65595 65596 65597 65598 65599
## [430] 65600 65601 65602 65603 65604 65605 65606 65607 65608 65609 65610
## [441] 65611 65612 65613 65614 65615 65616 65617 65618 65619 65620 65621
## [452] 65622 65623 65624 65625 65626 65627 65628 65629 65630 65631 65632
## [463] 65633 65634 65635 65636 65637 65638 65639 65640 65641 65642 65643
## [474] 65644 65645 65646 65647 65648 65649 65650 65651 65652 65653 65654
## [485] 65655 65656 65657 65658 65659 65660 65661 65662 65663 65664 65665
## [496] 65666 65667 65668 65669 65670 65671 65672 65673 65674 65675 65676
## [507] 65677 65678 65679 65680 65681 65682 65683 65684 65685 65686 65687
## [518] 65688 65689 65690 65691 65692 65693 65694 65695 65696 65697 65698
## [529] 65699 65700 65701 65702 65703 65704 65705 65706 65707 65708 65709
## [540] 65710 65711 65712 65713 65714 65715 65716 65717 65718 65719 65720
## [551] 65721 65722 65723 65724 65725 65726 65727 65728 65729 65730 65731
## [562] 65732 65733 65734 65735 65736 65737 65738 65739 65740 65741 65742
## [573] 65743 65744 65745 65746 65747 65748 65749 65750 65751 65752 65753
## [584] 65754 65755 65756 65757 65758 65759 65760 65761 65762 65763 65764
## [595] 65765 65766 65767 65768 65769 65770 65771 65772 65773 65774 65775
## [606] 65776 65777 65778 65779 65780 65781 65782 65783 65784 65785 65786
## [617] 65787 65788 65789 65790 65791 65792 65793 65794 65795 65796 65797
## [628] 65798 65799 65800 65801 65802 65803 65804 65805 65806 65807 65808
## [639] 65809 65810 65811 65812 65813 65814 65815 65816 65817 65818 65819
## [650] 65820 65821 65822 65823 65824 65825 65826 65827 65828 65829 65830
## [661] 65831 65832 65833 65834 65835 65836 65837 65838 65839 65840 65841
## [672] 65842 65843 65844 65845 65846 65847 65848 65849 65850 65851 65852
## [683] 65853 65854 65855 65856 65857 65858 65859 65860 65861 65862 65863
## [694] 65864 65865 65866 65867 65868 65869 65870 65871 65872 65873 65874
## [705] 65875 65876 65877 65878 65879 65880 65881 65882 65883 65884 65885
## [716] 65886 65887 65888 65889 65890 65891 65892 65893 65894 65895 65896
## [727] 65897 65898 65899 65900 65901 65902 65903 65904 65905 65906 65907
## [738] 65908 65909 65910 65911 65912 65913 65914 65915 65916 65917 65918
## [749] 65919 65920 65921 65922 65923 65924 65925 65926 65927 65928 65929
## [760] 65930 65931 65932 65933 65934 65935 65936 65937 65938 65939 65940
## [771] 65941 65942 65943 65944 65945 65946 65947 65948 65949 65950 65951
## [782] 65952 65953 65954 65955 65956 65957 65958 65959 65960 65961 65962
## [793] 65963 65964 65965 65966 65967 65968 65969 65970 65971 65972 65973
## [804] 65974 65975 65976 65977 65978 65979 65980 65981 65982 65983 65984
## [815] 65985 65986 65987 65988 65989 65990 65991 65992 65993 65994 65995
## [826] 65996 65997 65998 65999 66000 66001 66002 66003 66004 66005 66006
## [837] 66007 66008 66009 66010 66011 66012 66013 66014 66015 66016 66017
## [848] 66018 66019 66020 66021 66022 66023 66024 66025 66026 66027 66028
## [859] 66029 66030 66031 66032 66033 66034 66035 66036 66037 66038 66039
## [870] 66040 66041 66042 66043 66044 66045 66046 66047 66048 66049 66050
## [881] 66051 66052 66053 66054 66055 66056 66057 66058 66059 66060 66061
## [892] 66062 66063 66064 66065 66066 66067 66068 66069 66070 66071 66072
## [903] 66073 66074 66075 66076 66077 66078 66079 66080 66081 66082 66083
## [914] 66084 66085 66086 66087 66088 66089 66090 66091 66092 66093 66094
## [925] 66095 66096 66097 66098 66099 66100 66101 66102 66103 66104 66105
## [936] 66106 66107 66108 66109 66110 66111 66112 66113 66114 66115 66116
## [947] 66117 66118 66119 66120 66121 66122 66123 66124 66125 66126 66127
## [958] 66128 66129 66130 66131 66132 66133 66134 66135 66136 66137 66138
## [969] 66139 66140 66141 66142 66143 66144 66145 66146 66147 66148 66149
## [980] 66150 66151 66152 66153 66154 66155 66156 66157 66158 66159 66160
## [991] 66161 66162 66163 66164 66165 66166 66167 66168 66169 66170 66171
## [1002] 66172 66173 66174 66175 66176 66177 66178 66179 66180 66181 66182
## [1013] 66183 66184 66185 66186 66187 66188 66189 66190 66191 66192 66193
## [1024] 66194 66195 66196 66197 66198 66199 66200 66201 66202 66203 66204
## [1035] 66205 66206 66207 66208 66209 66210 66211 66212 66213 66214 66215
## [1046] 66216 66217 66218 66219 66220 66221 66222 66223 66224 66225 66226
## [1057] 66227 66228 66229 66230 66231 66232 66233 66234which(is.na(time_series_long_joined$Deaths))## integer(0)library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, uniontime_series_long_joined$Date <- mdy(time_series_long_joined$Date)time_series_long_joined_counts <- time_series_long_joined %>%
pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
names_to = "Report_Type", values_to = "Counts")
head(time_series_long_joined_counts)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Report_Type Counts
## <chr> <chr> <dbl> <dbl> <date> <chr> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 Confirmed 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-22 Deaths 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-23 Confirmed 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-23 Deaths 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-24 Confirmed 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-24 Deaths 0Making Graphs from the time series data
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Deaths")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","Japan", "Korea, South",
"Italy","Spain", "US")) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","France","Italy",
"Korea, South", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Deaths")
time_series_long_joined_counts %>%
group_by(Country_Region, Report_Type, Date) %>%
summarise(Counts = sum(Counts)) %>%
filter (Country_Region == "US") %>%
ggplot(aes(x = Date, y = log2(Counts), fill = Report_Type, color = Report_Type)) +
geom_point() +
geom_line() +
ggtitle("US COVID-19 Cases")## `summarise()` regrouping output by 'Country_Region', 'Report_Type' (override with `.groups` argument)
Exercise 1
See Lab5(part2datavisual).Rmd
Exercise 2
Confirmed_State_6_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/06-13-2020.csv")) %>%
filter (Country_Region == "US") %>%
group_by(Province_State, Country_Region) %>%
summarise(Confirmed = sum(Confirmed)) ## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_6_13)Confirmed_State_9_13 <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/09-13-2020.csv")) %>% filter(Country_Region == "US") %>% group_by(Province_State, Country_Region) %>% summarise(Confirmed = sum(Confirmed))## Parsed with column specification:
## cols(
## FIPS = col_double(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Last_Update = col_datetime(format = ""),
## Lat = col_double(),
## Long_ = col_double(),
## Confirmed = col_double(),
## Deaths = col_double(),
## Recovered = col_double(),
## Active = col_double(),
## Combined_Key = col_character(),
## Incidence_Rate = col_double(),
## `Case-Fatality_Ratio` = col_double()
## )## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)str(Confirmed_State_9_13)setdiff(Confirmed_State_6_13$Province_State, Confirmed_State_9_13$Province_State)## character(0)Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State"))
head(Confirmed_State_6_13_9_13_joined)## # A tibble: 6 x 5
## # Groups: Province_State [6]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Alabama US 24601 US 138755
## 2 Alaska US 653 US 6268
## 3 Arizona US 34660 US 208512
## 4 Arkansas US 12095 US 70219
## 5 California US 150018 US 761728
## 6 Colorado US 29002 US 61293tail(Confirmed_State_6_13_9_13_joined, 5)## # A tibble: 5 x 5
## # Groups: Province_State [5]
## Province_State Country_Region.x Confirmed.x Country_Region.y Confirmed.y
## <chr> <chr> <dbl> <chr> <dbl>
## 1 Virginia US 53869 US 133742
## 2 Washington US 25538 US 79826
## 3 West Virginia US 2274 US 12705
## 4 Wisconsin US 22518 US 89185
## 5 Wyoming US 1050 US 4346which(is.na(Confirmed_State_6_13_9_13_joined))## integer(0)Confirmed_State_6_13_9_13_joined <- full_join(Confirmed_State_6_13,
Confirmed_State_9_13, by = c("Province_State")) %>%
rename(Confirmed_6_13_2020 = "Confirmed.x", Confirmed_9_13_2020 = "Confirmed.y") %>%
select(-Country_Region.x, -Country_Region.y) %>%
replace_na(list(Confirmed_6_13_2020 = 0))
head(Confirmed_State_6_13_9_13_joined)## # A tibble: 6 x 3
## # Groups: Province_State [6]
## Province_State Confirmed_6_13_2020 Confirmed_9_13_2020
## <chr> <dbl> <dbl>
## 1 Alabama 24601 138755
## 2 Alaska 653 6268
## 3 Arizona 34660 208512
## 4 Arkansas 12095 70219
## 5 California 150018 761728
## 6 Colorado 29002 61293which(is.na(Confirmed_State_6_13_9_13_joined))## integer(0)Confirmed_State_6_13_9_13_joined_long <- Confirmed_State_6_13_9_13_joined %>%
pivot_longer(-c(Province_State),
names_to = "Date", values_to = "Confirmed")
Confirmed_State_6_13_9_13_joined_long ## # A tibble: 116 x 3
## # Groups: Province_State [58]
## Province_State Date Confirmed
## <chr> <chr> <dbl>
## 1 Alabama Confirmed_6_13_2020 24601
## 2 Alabama Confirmed_9_13_2020 138755
## 3 Alaska Confirmed_6_13_2020 653
## 4 Alaska Confirmed_9_13_2020 6268
## 5 Arizona Confirmed_6_13_2020 34660
## 6 Arizona Confirmed_9_13_2020 208512
## 7 Arkansas Confirmed_6_13_2020 12095
## 8 Arkansas Confirmed_9_13_2020 70219
## 9 California Confirmed_6_13_2020 150018
## 10 California Confirmed_9_13_2020 761728
## # … with 106 more rows##Exercise 2
ggplot(Confirmed_State_6_13_9_13_joined_long, aes(x = Province_State, y = Confirmed, fill = Date)) +
geom_bar(stat = "identity") 
##Exercise 3
ggplot(Confirmed_State_6_13_9_13_joined_long, aes(x = Province_State, y = Confirmed, fill = Date)) +
geom_bar(stat = "identity") + labs(title = "US Confimed COVID-19 Cases from 6/13/20 and 9/13/20",
x = "State/Province in US",
y = "Number of Confirmed Cases")
Exercise 4
time_series_long_joined_counts %>% group_by(Country_Region) %>%
ggplot(aes(x = Date, y = Counts)) + geom_point() + labs(x = "Date", y = "Deaths", title ="COVID-19 Deaths Worldwide")## Warning: Removed 2128 rows containing missing values (geom_point).
##Exercise 5
head(time_series_long_joined)## # A tibble: 6 x 7
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <date> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 0 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-23 0 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-24 0 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-25 0 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-26 0 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-27 0 0time_series_long_joined_Deaths_Confirmed <- time_series_long_joined %>%
mutate(Deaths_over_Confirmed = Deaths / Confirmed)
head(time_series_long_joined_Deaths_Confirmed)## # A tibble: 6 x 8
## Province_State Country_Region Lat Long Date Confirmed Deaths
## <chr> <chr> <dbl> <dbl> <date> <dbl> <dbl>
## 1 <NA> Afghanistan 33.9 67.7 2020-01-22 0 0
## 2 <NA> Afghanistan 33.9 67.7 2020-01-23 0 0
## 3 <NA> Afghanistan 33.9 67.7 2020-01-24 0 0
## 4 <NA> Afghanistan 33.9 67.7 2020-01-25 0 0
## 5 <NA> Afghanistan 33.9 67.7 2020-01-26 0 0
## 6 <NA> Afghanistan 33.9 67.7 2020-01-27 0 0
## # … with 1 more variable: Deaths_over_Confirmed <dbl>##Exercise 6
time_series_long_joined_Deaths_Confirmed %>%
ggplot(aes(x = Date, y = Deaths_over_Confirmed)) +
geom_point() +
ggtitle("COVID-19 US Death/Confirmed Cases Per Day")## Warning: Removed 11376 rows containing missing values (geom_point).
##Exercise 7
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("United Kingdom","France","Italy","Brazil", "India","Peru", "Spain", "Iran", "Mexico", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) + geom_point() +
geom_line() +
ggtitle("COVID-19 Top 10 Death Totals")
## Exercise 8
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("United Kingdom","France","Italy","Brazil", "India", "Peru", "Spain", "Iran", "Mexico", "US")) %>%
ggplot(aes(x = Date, y = Deaths, color = Country_Region)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Top 10 Death Totals") +
facet_wrap(~Country_Region, ncol=2, scales="free_y")
## Exercise 9
time_series_US_confirmed <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv")) ## Parsed with column specification:
## cols(
## .default = col_double(),
## iso2 = col_character(),
## iso3 = col_character(),
## Admin2 = col_character(),
## Province_State = col_character(),
## Country_Region = col_character(),
## Combined_Key = col_character()
## )## See spec(...) for full column specifications.head(time_series_US_confirmed)## # A tibble: 6 x 260
## UID iso2 iso3 code3 FIPS Admin2 Province_State Country_Region Lat
## <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl>
## 1 8.40e7 US USA 840 1001 Autau… Alabama US 32.5
## 2 8.40e7 US USA 840 1003 Baldw… Alabama US 30.7
## 3 8.40e7 US USA 840 1005 Barbo… Alabama US 31.9
## 4 8.40e7 US USA 840 1007 Bibb Alabama US 33.0
## 5 8.40e7 US USA 840 1009 Blount Alabama US 34.0
## 6 8.40e7 US USA 840 1011 Bullo… Alabama US 32.1
## # … with 251 more variables: Long_ <dbl>, Combined_Key <chr>,
## # `1/22/20` <dbl>, `1/23/20` <dbl>, `1/24/20` <dbl>, `1/25/20` <dbl>,
## # `1/26/20` <dbl>, `1/27/20` <dbl>, `1/28/20` <dbl>, `1/29/20` <dbl>,
## # `1/30/20` <dbl>, `1/31/20` <dbl>, `2/1/20` <dbl>, `2/2/20` <dbl>,
## # `2/3/20` <dbl>, `2/4/20` <dbl>, `2/5/20` <dbl>, `2/6/20` <dbl>,
## # `2/7/20` <dbl>, `2/8/20` <dbl>, `2/9/20` <dbl>, `2/10/20` <dbl>,
## # `2/11/20` <dbl>, `2/12/20` <dbl>, `2/13/20` <dbl>, `2/14/20` <dbl>,
## # `2/15/20` <dbl>, `2/16/20` <dbl>, `2/17/20` <dbl>, `2/18/20` <dbl>,
## # `2/19/20` <dbl>, `2/20/20` <dbl>, `2/21/20` <dbl>, `2/22/20` <dbl>,
## # `2/23/20` <dbl>, `2/24/20` <dbl>, `2/25/20` <dbl>, `2/26/20` <dbl>,
## # `2/27/20` <dbl>, `2/28/20` <dbl>, `2/29/20` <dbl>, `3/1/20` <dbl>,
## # `3/2/20` <dbl>, `3/3/20` <dbl>, `3/4/20` <dbl>, `3/5/20` <dbl>,
## # `3/6/20` <dbl>, `3/7/20` <dbl>, `3/8/20` <dbl>, `3/9/20` <dbl>,
## # `3/10/20` <dbl>, `3/11/20` <dbl>, `3/12/20` <dbl>, `3/13/20` <dbl>,
## # `3/14/20` <dbl>, `3/15/20` <dbl>, `3/16/20` <dbl>, `3/17/20` <dbl>,
## # `3/18/20` <dbl>, `3/19/20` <dbl>, `3/20/20` <dbl>, `3/21/20` <dbl>,
## # `3/22/20` <dbl>, `3/23/20` <dbl>, `3/24/20` <dbl>, `3/25/20` <dbl>,
## # `3/26/20` <dbl>, `3/27/20` <dbl>, `3/28/20` <dbl>, `3/29/20` <dbl>,
## # `3/30/20` <dbl>, `3/31/20` <dbl>, `4/1/20` <dbl>, `4/2/20` <dbl>,
## # `4/3/20` <dbl>, `4/4/20` <dbl>, `4/5/20` <dbl>, `4/6/20` <dbl>,
## # `4/7/20` <dbl>, `4/8/20` <dbl>, `4/9/20` <dbl>, `4/10/20` <dbl>,
## # `4/11/20` <dbl>, `4/12/20` <dbl>, `4/13/20` <dbl>, `4/14/20` <dbl>,
## # `4/15/20` <dbl>, `4/16/20` <dbl>, `4/17/20` <dbl>, `4/18/20` <dbl>,
## # `4/19/20` <dbl>, `4/20/20` <dbl>, `4/21/20` <dbl>, `4/22/20` <dbl>,
## # `4/23/20` <dbl>, `4/24/20` <dbl>, `4/25/20` <dbl>, `4/26/20` <dbl>,
## # `4/27/20` <dbl>, `4/28/20` <dbl>, …time_series_US_confirmed_long <- time_series_US_confirmed %>% group_by(Province_State) %>% select(-c(UID,iso2, iso3, code3, FIPS, Admin2, Lat, Long_,Country_Region, Combined_Key)) %>% pivot_longer(-c(Province_State)) %>% group_by(Province_State, name) %>% summarise(value = sum(value))## `summarise()` regrouping output by 'Province_State' (override with `.groups` argument)time_series_US_confirmed_long %>% group_by(Province_State) %>% ggplot(aes(x = name, y = value, color = Province_State)) +
geom_point() + facet_wrap(~Province_State, ncol = 5) + labs(x = "Date", y = "Confirmed", title = "COVID-19 US States and Territories Confirmed") 
##Exercise 10
time_series_long_joined %>%
group_by(Country_Region,Date) %>%
summarise_at(c("Confirmed", "Deaths"), sum) %>%
filter (Country_Region %in% c("China","France","Italy","Brazil", "India", "UK", "Peru", "Spain", "Iran", "Mexico", "US")) %>%
ggplot(aes(x = Date, y = Deaths)) +
geom_point() +
geom_line() +
ggtitle("COVID-19 Top 10 Death Totals") +
facet_wrap(~Country_Region, ncol=2, scales="free_y") +
theme_dark()